检索结果-内蒙古大学图书馆

Fractional Order Differential Evolution

IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Wang, Kaiyu Gao, Shangce Zhou, MengChu Zhan, Zhi-Hui Cheng, Jiujun Faculty of Engineering University of Toyama Toyama-shi Japan Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA College of Artificial Intelligence Nankai University Tianjin China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Differential evolution (DE) is a widely recognized method to solve complex optimization problems as shown by many researchers. Yet, non-adaptive versions of DE suffer from insufficient exploration ability and uses no historical information for its performance enhancement. This work proposes Fractional Order Differential Evolution (FODE) to enhance DE performance from two aspects. Firstly, a bi-strategy co-deployment framework is proposed. The population-based and parameter-based strategies are combined to leverage their respective advantages. Secondly, the fractional order calculus is first applied to the differential vector to enhance DE’s exploration ability by using the historical information of populations, and ensures the diversity of population in an evolutionary process. We use the 2017 IEEE Congress on Evolutionary Computation (CEC) test functions, and CEC2011 real-world problems to evaluate FODE’s performance. Its sensitivity to parameter changes is discussed and an ablation study of multi-strategies is systematically performed. Furthermore, the variations of exploration and exploitation in FODE are visualized and analyzed. Experimental results show that FODE is superior to other state-of-the-art DE variants, the winners of CEC competitions, other fractional order calculus-based algorithms, and some powerful variants of classic algorithms. IEEE

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

Cambricon-QR:a sparse and bitwise reproducible quantized training accelerator

引用

High technology Letters 2024年第1期30卷 52-60页

作者：李楠 ZHAO Yongwei ZHI Tian LIU Chang DU Zidong HU Xing LI Wei ZHANG Xishan LI Ling SUN Guangzhong School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026P.R.China State Key Laboratory of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijing 100086P.R.China Cambricon Tech.Ltd Beijing 100191P.R.China Institute of Software Chinese Academy of SciencesBeijing 100086P.R.China

Quantized training has been proven to be a prominent method to achieve deep neural network training under limited computational *** uses low bit-width arithmetics with a proper scaling factor to achieve negligible accuracy ***-Q is the ASIC design proposed to efficiently support quantized training,and achieves significant performance ***,there are still two caveats in the ***,Cambricon-Q with different hardware specifications may lead to different numerical errors,resulting in non-reproducible behaviors which may become a major concern in critical ***,Cambricon-Q cannot leverage data sparsity,where considerable cycles could still be squeezed *** address the caveats,the acceleration core of Cambricon-Q is redesigned to support fine-grained irregular data *** new design not only enables acceleration on sparse data,but also enables performing local dynamic quantization by contiguous value ranges(which is hardware independent),instead of contiguous addresses(which is dependent on hardware factors).Experimental results show that the accuracy loss of the method still keeps negligible,and the accelerator achieves 1.61×performance improvement over Cambricon-Q,with about 10%energy increase.

关键词： quantized training sparse accelerator Cambricon-QR

来源：评论

学校读者我要写书评

暂无评论

Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs

Context-Driven Index Trimming: A Data Quality Perspective to...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ma, Kexin Jin, Ruochun Wang, Haotian Wang, Xi Chen, Huan Tang, Yuhua Wang, Qian Institute for Quantum Information State Key Laboratory of High Performance Computing China College of Computer Science and Technology National University of Defense Technology Changsha China Intelligent Game and Decision Lab Academy of Military Science Beijing China

ISBN: (纸本)9798891761681

Retrieval-Augmented Large Language Models (RALMs) have made significant strides in enhancing the accuracy of generated responses. However, existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods. We propose to boost the precision of RALMs' answers from a data quality perspective through the Context-Driven Index Trimming (CDIT) framework, where Context Matching Dependencies (CMDs) are employed as logical data quality rules to capture and regulate the consistency between retrieved contexts. Based on the semantic comprehension capabilities of Large Language Models (LLMs), CDIT can effectively identify and discard retrieval results that are inconsistent with the query context and further modify indexes in the database, thereby improving answer quality. Experiments demonstrate average improvement of 3.75% in accuracy on challenging question-answering tasks. Also, the flexibility of CDIT is verified through its compatibility with various language models and indexing methods, which offers a promising approach to bolster RALMs' data quality and retrieval precision jointly. © 2024 Association for Computational Linguistics.

关键词： Data accuracy

来源：评论

学校读者我要写书评

暂无评论

A New Method for Predefined-Time Synchronization Control of Memristive Cohen-Grossberg Neural Networks

A New Method for Predefined-Time Synchronization Control of ...

引用

2023 IEEE International Conference on Memristive computing and Applications, ICMCA 2023

作者： Liu, Aidi Yu, Wenyue Huang, Lili Guo, Jian Lei, Tengfei Zhao, Hui School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology China Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology China School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9798350316230

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory. © 2023 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Makespan and Security-Aware Workflow Scheduling for Cloud Service Cost Minimization Using Firefly Optimizer 22nd

Makespan and Security-Aware Workflow Scheduling for Cloud ...

引用

22nd International Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2022

作者： Zhou, Chengliang Wang, Tian Li, Liying Sun, Jin Zhou, Junlong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai200062 China

ISBN: (纸本)9783031226762

As efficient commercial information technology, cloud computing has attracted more and more users to submit their requests to the cloud platform and pay for them based on the amount and quality of services. One of the most important concerns of cloud service providers is to minimize the monetary cost for completing users’ requests while meeting constraints on the quality of services such as makespan and security. To solve this concern, this paper proposes a makespan and security-aware workflow scheduling method based on an improved firefly optimizer that consists of a new solution initialization scheme, a firefly position updating scheme, a firefly-to-solution mapping operator, and two task-to-virtual machine assignment schemes. We carry out extensive simulation experiments on real-world workflows to validate our proposed workflow scheduling method. The results reveal that the monetary cost reduction achieved by our method can be up to 54.0% compared to a baseline and two state-of-the-art approaches. © 2023, Springer Nature Switzerland AG.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Data quantity governance for machine learning in materials science

引用

National Science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

来源：评论

学校读者我要写书评

暂无评论

Ethereum Phishing Detection Using Hyperbolic Neural Networks and Temporal Information 22

Ethereum Phishing Detection Using Hyperbolic Neural Networks...

引用

22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Xu, Rong Zhang, Jun Zhu, Xiaoyue Song, Zhaoxiong Yu, Zhi Inner Mongolia University College of Computer Science Hohhot China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331509712

In recent years, the frequent occurrence of phishing scams on Ethereum has posed serious threats to transaction security and the financial safety of users. This paper proposes an Ethereum phishing scam detection method based on Hyperbolic Neural Networks (HGNNs) and temporal information. The method maps the Ethereum transaction network to hyperbolic space for structural feature extraction, effectively capturing hierarchical structures and complex relationships within the graph, thereby improving the accuracy of phishing scam detection. The model includes a structural feature extraction module and a temporal feature extraction module. It uses HGNN and self-attention mechanism to extract the structural features of the transaction graph, and uses a multi-head attention mechanism to capture the dynamic evolution pattern of the graph. Experimental validation on real Ethereum datasets demonstrates that the proposed model outperforms benchmark models, showcasing its effectiveness. © 2024 IEEE.

关键词： Phishing

来源：评论

学校读者我要写书评

暂无评论

A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

引用

Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

来源：评论

学校读者我要写书评

暂无评论

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation 38

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmenta...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Faster Algorithm for the 4-Coloring Problem 32

A Faster Algorithm for the 4-Coloring Problem

引用

32nd Annual European Symposium on Algorithms, ESA 2024

作者： Wu, Pu Gu, Huanyu Jiang, Huiqin Shao, Zehui Xu, Jin School of Computer Science Peking University Beijing China Institute of Computing Science and Technology Guangzhou University China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education Beijing China

ISBN: (纸本)9783959773386

We explore the 4-coloring problem, a fundamental combinatorial NP-hard problem. Given a graph G, the 4-coloring problem asks whether there exists a function f from the vertex set of G to {1, 2, 3, 4} such that f(u) ≠ f(v) for each edge uv of G. Such function f is referred to as a 4-coloring of G. The fastest known algorithm for the 4-coloring problem, introduced by Fomin, Gaspers, and Saurabh (COCOON 2007), exhibits a time complexity of O(1.7272n) and exponential space. In this paper, we propose an enhanced algorithm for the 4-coloring problem with a time complexity of O(1.7159n) and polynomial space. Our algorithm is deterministic and built upon a novel method. Specifically, inspired by previous algorithmic approaches for the 4-coloring problem, such as the aforementioned O(1.7272n) time algorithm, we consider the instance (G, I, S), where G is a graph and I, S are subsets of its vertex set representing vertices colored with 1 and vertices unable to be colored with 1, respectively. For a given instance (G, I, S), we aim to determine the existence of a 4-coloring f of G such that f(v) = 1 for v ∈ I and f(v) ≠ 1 for v ∈ S. Our key innovation lies in recognizing that, leveraging certain combinatorial properties, the instance (G, I, S) can be efficiently solved when G − I − S is a union of K3's and K4's (where K3 and K4 denote complete graphs with 3 and 4 vertices, respectively). The ability to efficiently solve instances (G, I, S), where G − I − S is comprised solely of K3's and K4's, enables us to devise a branching algorithm capable of efficiently addressing instances (G, I, S), where G − I − S is not a union of K3's and K4's (the other case). Based on this innovative method, we derive our final enhanced algorithm. © Pu Wu, Huanyu Gu, Huiqin Jiang, Zehui Shao, and Jin Xu;licensed under Creative Commons License CC-BY 4.0.

关键词： Graph algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：